# Hough-SIFT: Robust Image Registration for Linear Structures via Hough Space

arXiv: https://arxiv.org/abs/2607.14598 · HTML: https://mlsanity.com/paper/2607.14598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)

## Built on by (most cited first, 1 of 1)

- [HOME: Robust Hough-space Matching Method for Structured and Textureless Videos](https://mlsanity.com/paper/2607.25389/) (2026)

## Abstract (arXiv)

Image registration is essential in applications such as electronic image stabilization. Scale-Invariant Feature Transform (SIFT), a widely used local keypoint detector and descriptor, typically provides accurate registration; however, it often fails in scenes with strong linear structures (e.g., shutters), where local features become ambiguous. We propose Hough-SIFT, a robust registration method that performs SIFT descriptor matching in Hough space. In this domain, linear structures form distinctive peaks that restore descriptor discriminability. Experiments demonstrate that Hough-SIFT is robust in linear scenes where SIFT frequently fails, while maintaining accuracy comparable to SIFT in normal scenes.